Building Management Systems (BMS) are well established and are used to control heating and air conditioning (AC) infrastructure so as to ensure the comfort of building inhabitants as well as to achieve other goals such as cooling of ICT equipment. In the business and corporate environment, BMS are available that can be used to obtain sensor data and set control values such as thermostat set points, remotely (e.g. see: www.trendcontrols.com).
In tandem with this, energy savings (and associated cost savings) have become an important goal in recent years. Clearly there can be a trade-off between energy saving and controlling temperature using HVAC (heating, ventilation and air-conditioning) systems under BMS control. For example, reducing a thermostat setting by one degree in the UK winter will save energy, but it will typically lead to a higher percentage of dissatisfied building inhabitants.
The broader context of energy saving is energy management (EM). Here the goal is not just to reduce energy but rather to manage the consumption of energy over time to achieve certain goals. Specifically techniques such as Demand Side Management (DSM) or Demand Response (DR) can be used to time-shift energy consumption, typically to avoid peaks in energy consumption, so as to help balance energy consumption with available energy supply. Peak periods of energy consumption in the UK and many other countries equate to more costly energy generation, so reduction of demand peaks (which would otherwise exceed normally available supply) can result in avoiding buying energy at the most costly time or can result in being paid revenue by the energy provider or networks (National Grid in the UK) so that they can similarly avoid direct costs themselves. An example of this is UK National Grid's Short Term Operating Reserve (STOR) scheme.
Note that when applying climate control to buildings there is a desire to control “thermal comfort” which has been defined by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). A model for thermal comfort is captured in terms of PMV (predicted mean vote) and PPD (predicted percent dissatisfied) measures via equations defined in ISO 7730. This model has been used, with respect to temperature variability, to inform the measure of temperature-drift-related discomfort discussed later.
The Demand Response Research Center (DRRC, Lawrence Berkeley National Laboratory) has published a comprehensive report “Introduction to Commercial Building Control Strategies and Techniques for Demand Response,” which can be found at gaia.lbl.gov.
This guide gives a good summary of available approaches and in particular discusses the following DR strategies for HVAC systems:                Global Temperature Adjustment of Zones        Systemic Adjustments to the Air Distribution and/or Cooling Systems.        
Section 3.4.1 of this report states that: “Demand limit strategy is a supervisory control algorithm that manages a combination of single or multiple DR control strategies. When the whole building demand exceeds a warning level, the EMCS deploys strategy #1. If the whole building demand still exceeds the warning level, strategy #2 is deployed, and so on. Thus, whenever the demand hits the warning level, the whole building demand is suppressed by a combination of sequential strategies. Strategies that have a lower impact on occupants' comfort should come first, and strategies that have more impact should come later. When the demand goes below the lower deadband level, the last strategy should be deployed. For any strategy that may have a risk of causing a rebound peak, slow recovery strategies must be considered.
Demand limit strategy has been considered as a method to avoid high demand charges during normal operation, rather than as a demand response strategy. However, depending on the structure of demand response programs, demand limit strategy can be a very useful tool to achieve desired kW savings. For example, demand bidding programs offered by many utility companies require curtailing a preset kW demand against a baseline defined by each utility. If the EMCS has a function to develop dynamic demand limit setpoints based on the baseline, the demand limit target can be set as shown in Equation 1, so that the desired demand savings can always be achieved.[Demand limit target]=[Baseline]−[Desired demand savings]”  Equation 1:
Control of HVAC units/infrastructure has been proposed that is sensitive to occupancy of buildings. E.g. “Occupancy Based Demand Response HVAC Control Strategy,” which can be found at andes.ucmerced.edu. This can help promote energy saving but does not seek to meet a specific energy cap.
It is an object of the present invention to provide systems and methods in which the aggregated power load of a plurality of appliances is capped to a selected value (which may be arbitrary, or may be dictated by conditions) whilst seeking to minimize the deviation from target environmental conditions.
It is a further object of the present invention to provide a building management system, a method of operation and individual climate control appliances which provide for enhanced flexibility of appliance operation with a guarantee that the total load will not exceed a certain “cap”.
Whilst the aims of the present invention are most relevant to commercial buildings, they could in principle be applied in a residential context too, where the goal might be to meet an energy cap across a number of rooms or more likely a number of households. In the latter case it might be part of a lower tariff energy package where DR is permitted by a householder so as to have a better deal on energy costs.
Aspects of the present invention could also be used to help identify and prioritise which rooms or areas within a building which could have reduced A/C (or heating, or other climate control) whilst still maintaining an acceptable temperature range, thereby contributing into a DR/DSM scheme.